The temperature outside and the amount of sunlight in a day are likely to be highly correlated, as more sunlight generally corresponds to higher temperatures. This is an example of multicollinearity.

A data point that lies outside the overall distribution of the dataset is called an outlier. These are unusual observations that differ significantly from the other data points.

Multicollinearity refers to a situation where two or more independent variables in a multiple regression model are highly correlated. If these variables are closely correlated, it can be hard for the model to determine the effect of each variable independently, which may lead to unstable estimates.

Aesthetics in data visualization involve multiple factors including balance (equal weightage to all parts), simplicity (avoiding unnecessary complexity), clarity (clearly understandable), and the color scheme (which can direct attention, represent categories, or express quantities). Good aesthetics make the data easy to understand and the message memorable.

For MCAR data, Random Sample Imputation is a good choice as it assumes that the data are missing completely at random. It works by taking random observations from the dataset and using these to replace the missing values.

When data is skewed to the right, it means there are a number of observations with large values, which pull the "Mean" up, making it greater than the median.

Matplotlib provides a low-level, object-oriented API for embedding plots into applications and gives the most control over every aspect of the figure properties. This makes it suitable for creating publication-quality figures.

One of the most common reasons a Histogram might appear biased is due to an improper choice of bin width. The bin width greatly affects the resulting shape and patterns. If the bins are too wide, important features may be hidden. If they are too narrow, the representation may appear too cluttered or noisy.

'Wrangling' in the EDA process typically involves cleaning the data and dealing with missing values and outliers. This step is crucial for preparing the data for subsequent exploration and analysis.

In the presence of outliers, the Pearson's correlation coefficient can provide misleading results. Pearson's correlation is sensitive to outliers and hence can be significantly affected by them.